{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jermwatt/machine_learning_refined/blob/main/notes/6_Linear_twoclass_classification/6_9_Weighted.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EyQ82Q1-P5R0"
   },
   "source": [
    "## Chapter 6: Linear two-class classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains interactive content from an early draft of the university textbook <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main\">\n",
    "Machine Learning Refined (2nd edition) </a>.\n",
    "\n",
    "The final draft significantly expands on this content and is available for <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main/chapter_pdfs\"> download as a PDF here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fNwFVoSGP5R4"
   },
   "source": [
    "# 6.9 Weighted Two-Class Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VQ7L23iCP5R5"
   },
   "source": [
    "Because our two-class classification cost functions are *summable over individual points* we can - as we did with regression in [Section 5.5](https://jermwatt.github.io/machine_learning_refined/notes/5_Linear_regression/5_5_Weighted.html) - weight individual points in order to emphasize or de-emphasize their importance to a classification model.  This is called *weighted classification*.  This idea is often implemented when dealing with *highly imbalanced* two class datasets, as we discuss here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9uRgEZJrP5R5",
    "outputId": "1c9e50dd-ef81-46d8-b0fc-989817f3dfc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: github-clone in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (1.2.0)\n",
      "Requirement already satisfied: requests>=2.20.0 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (2.32.3)\n",
      "Requirement already satisfied: docopt>=0.6.2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (0.6.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2024.12.14)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "chapter_6_datasets already cloned!\n",
      "chapter_6_library already cloned!\n",
      "chapter_6_videos already cloned!\n"
     ]
    }
   ],
   "source": [
    "# install github clone - allows for easy cloning of subdirectories\n",
    "!pip install github-clone\n",
    "from pathlib import Path \n",
    "\n",
    "# clone datasets\n",
    "if not Path('chapter_6_datasets').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/6_Linear_twoclass_classification/chapter_6_datasets\n",
    "else:\n",
    "    print('chapter_6_datasets already cloned!')\n",
    "\n",
    "# clone library subdirectory\n",
    "if not Path('chapter_6_library').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/6_Linear_twoclass_classification/chapter_6_library\n",
    "else:\n",
    "    print('chapter_6_library already cloned!')\n",
    "\n",
    "# clone videos\n",
    "if not Path('chapter_6_videos').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/6_Linear_twoclass_classification/chapter_6_videos\n",
    "else:\n",
    "    print('chapter_6_videos already cloned!')\n",
    "\n",
    "\n",
    "# append path for local library, data, and image import\n",
    "import sys\n",
    "sys.path.append('./chapter_6_library')\n",
    "sys.path.append('./chapter_6_datasets') \n",
    "sys.path.append('./chapter_6_videos') \n",
    "\n",
    "# import section helper\n",
    "import section_6_9_helpers\n",
    "\n",
    "# dataset paths\n",
    "data_path_1 = 'chapter_6_datasets/census_data.csv'\n",
    "data_path_2 = 'chapter_6_datasets/3d_classification_data_v2_mbalanced.csv'\n",
    "\n",
    "# video paths\n",
    "video_path_1 = 'chapter_6_videos/animation_2.mp4'\n",
    "\n",
    "# standard imports\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image, HTML\n",
    "from base64 import b64encode\n",
    "\n",
    "def show_video(video_path, width = 1000):\n",
    "    video_file = open(video_path, \"r+b\").read()\n",
    "    video_url = f\"data:video/mp4;base64,{b64encode(video_file).decode()}\"\n",
    "    return HTML(f\"\"\"<video width={width} controls><source src=\"{video_url}\"></video>\"\"\")\n",
    "\n",
    "# import autograd-wrapped numpy\n",
    "import autograd.numpy as np\n",
    "\n",
    "# this is needed to compensate for matplotlib notebook's tendancy to blow up images when plotted inline\n",
    "%matplotlib inline\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.autolayout'] = True\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X_vDQx8QP5R7"
   },
   "source": [
    "## Weighted two-class classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ecvWpUsLP5R8"
   },
   "source": [
    "Just as we saw with regression in [Section 5.5](https://jermwatt.github.io/machine_learning_refined/notes/5_Linear_regression/5_5_Weighted.html), weighted classification cost functions naturally arise due to epeated points in a dataset.  When collecting metadata (e.g., census data) it is not so uncommon collect duplicate entries - multiple people having similar/the same stats based on a given survey.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "97xcK70KP5R9"
   },
   "source": [
    "Below we take a standard census dataset and plot a subset of it along a single input feature.  With only one feature taken into account we end up with multiple entries of the same datapoint, which we show visually via the radius of each point (the more times a given datapoint appears in the dataset the larger we make the radius).  These datapoints should not be thrown away - they did not arise due to some error in data collecting / storage -  they represent the true dataset.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "ydhpoq4gP5R-",
    "outputId": "10adc7f0-ff4a-4d70-a62a-1df21446bf53"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m demo1 \u001b[38;5;241m=\u001b[39m section_6_9_helpers\u001b[38;5;241m.\u001b[39mstatic_visualizer()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdemo1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_path_1\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Desktop/machine-learning-refined/notes/6_Linear_twoclass_classification/./chapter_6_library/section_6_9_helpers.py:217\u001b[0m, in \u001b[0;36mstatic_visualizer.plot\u001b[0;34m(self, csvname)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m,csvname):\n\u001b[0;32m--> 217\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcsvname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# quantize x\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     x_quantized \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquantize(x)\n",
      "File \u001b[0;32m~/Desktop/machine-learning-refined/notes/6_Linear_twoclass_classification/./chapter_6_library/section_6_9_helpers.py:196\u001b[0m, in \u001b[0;36mstatic_visualizer.load_data\u001b[0;34m(self, csvname)\u001b[0m\n\u001b[1;32m    194\u001b[0m y \u001b[38;5;241m=\u001b[39m y_all[ind]\n\u001b[1;32m    195\u001b[0m x \u001b[38;5;241m=\u001b[39m X_all[ind]\n\u001b[0;32m--> 196\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(x, dtype\u001b[38;5;241m=\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat\u001b[49m)    \n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x, y\n",
      "File \u001b[0;32m~/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages/numpy/__init__.py:397\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    392\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    393\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn the future `np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` will be defined as the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    394\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorresponding NumPy scalar.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __former_attrs__:\n\u001b[0;32m--> 397\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr], name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __expired_attributes__:\n\u001b[1;32m    400\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    401\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` was removed in the NumPy 2.0 release. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    402\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m__expired_attributes__[attr]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    403\u001b[0m         name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    404\u001b[0m     )\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'float'.\n`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
     ]
    }
   ],
   "source": [
    "demo1 = section_6_9_helpers.static_visualizer()\n",
    "demo1.plot(data_path_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_oBsF3VNP5SA"
   },
   "source": [
    "Just as with a regression cost, if we examine any two-class classification cost it will 'collapse', with summands containing identical points combining naturally.  One can see this by performing the same kind of simple exercise used in [Section 5.5](https://jermwatt.github.io/machine_learning_refined/notes/5_Linear_regression/5_5_Weighted.html) to illustrate this fact for regression.  This leads to the notion of *weighting* two-class cost functions, like e.g., the weighted softmax below (written using our general `model` notation used)\n",
    "\n",
    "\\begin{equation}\n",
    "g\\left(\\mathbf{w}\\right) = \\sum_{p=1}^P\\beta_p\\,\\text{log}\\left(1 + e^{-y_p\\text{model}\\left(x_p,\\mathbf{w}\\right)}\\right).\n",
    "\\end{equation}\n",
    "\n",
    "Here the values $\\beta_1,\\,\\beta_2,\\,...,\\,\\beta_P$ are fixed *point-wise* weights.  That is, a unique point $\\left(x_p,\\,y_p\\right)$ in the dataset has weight $\\beta_p = 1$ whereas if this point is repeated $R$ times in the dataset then one instance of it will have weight $\\beta_p = R$ while the others have weight $\\beta_p = 0$.\n",
    "\n",
    "Since these weights are fixed (i.e., they are *not* parameters that need to be tuned, like $\\mathbf{w}$) we can minimize a weighted classification cost precisely as we would any other e.g,. via a local optimization scheme like gradient descent or Newton's method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "lOzmxxZoP5SB"
   },
   "outputs": [],
   "source": [
    "# a Python implementation of the weighted two-class softmax cost function\n",
    "# setup to compute over mini-batches if desired\n",
    "def softmax(w,x,y,beta,iter):\n",
    "    # get batch of points\n",
    "    x_p = x[:,iter]\n",
    "    y_p = y[:,iter]\n",
    "    beta_p = beta[:,iter]\n",
    "\n",
    "    # compute cost over batch        \n",
    "    cost = np.sum(beta_p*np.log(1 + np.exp(-y_p*model(x_p,w))))\n",
    "    return cost/float(np.size(y_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WC8boeK4P5SB"
   },
   "source": [
    "Just as with regression, we can also think of *assigning* these fixed weight values to points ourselves based on our 'confidence' of the legitimacy of a datapoint.  If we believe that a point is very trustworthy we can set its corresponding weight $\\beta_p$ closer to $1$, and the more untrustworthy we find a point the smaller we set $\\beta_p$ in the range $0 \\leq \\beta_p \\leq 1$ where $\\beta_p = 0$ implies we do not trust the point at all.  In making these weight selections we of course determine *how important each datapoint is* in the training of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CNp5ltrgP5SB"
   },
   "source": [
    "## Dealing with imbalanced datasets via weighted classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KhQJewtxP5SB"
   },
   "source": [
    "Weighted classification - in the manner detailed above - is often used to deal with *imbalanced datasets*.  These are datasets which contain far more examples of one class of data than the other.  With such datasets it is often easy to achieve a high accuracy by *misclassifying points from the smaller class*.  For example, if a two-class dataset consisted of $90\\%$ points with label value $-1$ and $10\\%$ points with label $+1$, then simply classifying all datapoints to the $-1$ class would provide $90\\%$ accuracy.  \n",
    "\n",
    "This idea of 'sacrificing' members of the smaller class by misclassifying them (instead of members from the majority class) is - depending on the application - not very desirable.  For example\n",
    "\n",
    "- if the classification determines whether or not someone has a particularly rare but deadly disease that requires further examination one would likely rather misclassify a healthly individual (and give them further testing) then miss someone with the disease\n",
    "\n",
    "\n",
    "- if the classification determines whether or not a particular financial transaction was *fraudulant*, one would likely rather misclassify a standard transaction (to review further or alert a customer) than miss an actually fraudulunt transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UvO01zu8P5SC"
   },
   "source": [
    "One way of ameliorating this issue is to use a weighted classification cost to alter the behavior of the learned classifier so that it weights points in the smaller class more, and points in the larger class less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xlJOtHK2P5SC"
   },
   "source": [
    "In order to produce this outcome it is common to assign such weigths *inversely proportional to the number of members of each class*.  That is if we denote $\\Omega_{+1}$ and $\\Omega{-1}$ index sets for the points in classes $+1$ and $-1$ respectively, then first note that $P = \\left\\vert \\Omega_{+1} \\right\\vert + \\left\\vert \\Omega_{-1} \\right\\vert$.  Then denoting $\\beta_{+1}$ and $\\beta_{-1}$ the weight for each member of class $+1$ and $-1$ respectively we can set these class-wise weights inversely proportional to the number of points in each class as\n",
    "\n",
    "\\begin{array}\n",
    "\\\n",
    "\\beta_{+1} = \\frac{1}{\\left\\vert \\Omega_{+1} \\right\\vert} \\\\\n",
    "\\beta_{-1} = \\frac{1}{\\left\\vert \\Omega_{-1} \\right\\vert}. \\\\\n",
    "\\end{array}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rr4igq8UP5SC"
   },
   "source": [
    "Below weight of minority class is increased as you move the animation slider from left to right, with new classification shown as result (and point size of minority red class increased for visualization).  As you increase the weighting on minority class members you incentivise learned classifier to classify these points correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "5SurMomwP5SC",
    "outputId": "2bb14e0f-3134-48a1-a9b7-9cb4616cd6b8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo4 = section_6_9_helpers.animation_visualizer()\n",
    "demo4.animate_weightings(video_path_1,data_path_2,num_slides = 50,fps=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "5wg8FyeBQtsy",
    "outputId": "a0560604-363e-46ca-be01-b593003d44da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=400 controls><source 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_video(video_path_1, width=400)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "86px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
